Global citation recommendation using knowledge graphs

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
3089-3100
Series
Journal of Intelligent and Fuzzy Systems, Volume 34, issue 5
Abstract
Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs - and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.
Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
Citation recommendations, knowledge graphs, recommender systems
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Citation
Ayala-Gomez , F , Daroczy , B , Benczur , A , Mathioudakis , M & Gionis , A 2018 , ' Global citation recommendation using knowledge graphs ' , Journal of Intelligent and Fuzzy Systems , vol. 34 , no. 5 , pp. 3089-3100 . https://doi.org/10.3233/JIFS-169493